A Learning Approach to the Prediction of Reliability Ranking for Web Services

Service computing is a popular development paradigm in information technology. The functional properties of Web services assure correct functionality of cloud applications, while the nonfunctional properties such as reliability might significantly influence the user-perceived availability evaluation. Reliability rankings provide valuable information for making optimal cloud service selection from a set of functionally-equivalent candidate services. There existed several approaches that can conduct reliability ranking prediction for Web services. Those approaches acquire different rankings with different preference functions. It is arduous to determine whether there exists the best one in them, and what is the best one if not. This paper proposes a learning approach to reliability ranking prediction for Web services which utilizes past service invocation logs to train preference function. To validate the proposed approach, large-scale experiments are conducted based on a real-world Web service dataset, WSDream. The results show that our proposed approach achieves higher prediction accuracy than the existing approaches.